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Lung cancer is considered the most common and the deadliest cancer type. Lung cancer could be mainly of 2 types: small cell lung cancer and non-small cell lung cancer. Non-small cell lung cancer is affected by about 85% while small cell lung cancer is only about 14%. Over the last decade, functional genomics has arisen as a revolutionary tool for studying genetics and uncovering changes in gene expression. RNA-Seq has been applied to investigate the rare and novel transcripts that aid in discovering genetic changes that occur in tumours due to different lung cancers. Although RNA-Seq helps to understand and characterise the gene expression involved in lung cancer diagnostics, discovering the biomarkers remains a challenge. Usage of classification models helps uncover and classify the biomarkers based on gene expression levels over the different lung cancers. The current research concentrates on computing transcript statistics from gene transcript files with a normalised fold change of genes and identifying quantifiable differences in gene expression levels between the reference genome and lung cancer samples. The collected data is analysed, and machine learning models were developed to classify genes as causing NSCLC, causing SCLC, causing both or neither. An exploratory data analysis was performed to identify the probability distribution and principal features. Due to the limited number of features available, all of them were used in predicting the class. To address the imbalance in the dataset, an under-sampling algorithm Near Miss was carried out on the dataset. For classification, the research primarily focused on 4 supervised machine learning algorithms: Logistic Regression, KNN classifier, SVM classifier and Random Forest classifier and additionally, 2 ensemble algorithms were considered: XGboost and AdaBoost. Out of these, based on the weighted metrics considered, the Random Forest classifier showing 87% accuracy was considered to be the best performing algorithm and thus was used to predict the biomarkers causing NSCLC and SCLC. The imbalance and limited features in the dataset restrict any further improvement in the model’s accuracy or precision. In our present study using the gene expression values (LogFC, P Value) as the feature sets in the Random Forest Classifier BRAF, KRAS, NRAS, EGFR is predicted to be the possible biomarkers causing NSCLC and ATF6, ATF3, PGDFA, PGDFD, PGDFC and PIP5K1C is predicted to be the possible biomarkers causing SCLC from the transcriptome analysis. It gave a precision of 91.3% and 91% recall after fine tuning. Some of the common biomarkers predicted for NSCLC and SCLC were CDK4, CDK6, BAK1, CDKN1A, DDB2.

Background: Episiotomy is the incision given over the pudendum, i.e. on the external genitalia organ during the vaginal delivery. Aims and Objective: In this study it has been tried to evaluate the benefits and the risks of selective episiotomy over spontaneous lacerations. Materials and Methods: This is an institution based interventional longitudinal study carried out in the Department of Obstetrics & Gynaecology of College Of Medicine & JNM Hospital, Kalyani over a period of 18 months in 218 patients (109 in each group) fulfilling the inclusion criteria. The recruitment and allocation in episiotomy and non-episiotomy groups were random after proper consent from the participants. Results: The frequency of postpartum perineal pain was around 47% in no episiotomy group and around 60% in selective episiotomy group. There were no cases of dehiscence, haematoma or wound infection in either of the groups. Around 96.22% of the women in the non-episiotomy group were satisfied or very satisfied compared to 89.52% in the selective episiotomy group. Conclusion: An episiotomy rate of less than 1% found in no episiotomy group as compared to around 18% episiotomy rate in selective episiotomy group. However, they have almost same feto-maternal outcome which successfully establish the effectiveness of no episiotomy practice over the selective one.

The World Health Organization has put drug resistance in tuberculosis on its list of significant threats, with a critical emphasis on resolving the genetic differences in Mycobacterium tuberculosis. This provides an opportunity for a better understanding of the evolutionary progression leading to anti-microbial resistance. Anti-microbial resistance has a great impact on the economic stability of the global healthcare sector. We performed a timeline genomic analysis from 2003 to 2021 of 578 mycobacterium genomes to understand the pattern underlying genomic variations. Potential drug targets based on functional annotation was subjected to pharmacophore-based screening of FDA-approved phyto-actives. Reaction search, MD simulations, and metadynamics studies were performed. A total of 4,76,063 mutations with a transition/transversion ratio of 0.448 was observed. The top 10 proteins with the least number of mutations were high-confidence drug targets. Aminoglycoside 2′-N-acetyltransferase protein (AAC2′), conferring resistance to aminoglycosides, was shortlisted as a potential drug target based on its function and role in bait drug synergism. Gentamicin-AAC2′ binding pose was used as a pharmacophore template to screen 10,570 phyto-actives. A total of 66 potential hits were docked to obtain naloxone as a lead—active with a docking score of −6.317. Naloxone is an FDA-approved drug that rapidly reverses opioid overdose. This is a classic case of a repurposed phyto-active. Naloxone consists of an amine group, but the addition of the acetyl group is unfavorable, with a reaction energy of 612.248 kcal/mol. With gentamicin as a positive control, molecular dynamic simulation studies were performed for 200 ns to check the stability of binding. Metadynamics-based studies were carried out to compare unbinding energy with gentamicin. The unbinding energies were found to be −68 and −74 kcal/mol for naloxone and gentamycin, respectively. This study identifies naloxone as a potential drug candidate for a bait drug synergistic approach against Mycobacterium tuberculosis.

Polyester (PET) fabric was treated with polyvinyl alcohol (PVA) in alkaline medium. The moisture regain, water retention and wickability of the PVA treated polyester fabric were tested. The presence of PVA in the treated PET fabric was assessed by spot test. The treated fabric was also characterized by scanning electron microscope (SEM). The PVA treated polyester fabric showed improved hydrophilic character over intact and sodium hydroxide treated PET fabrics.

Small molecules with antioxidative properties have been implicated in amyloid disorders. Curcumin is the active ingredient present in turmeric and known for several biological and medicinal effects. Adequate evidence substantiates the importance of curcumin in Alzheimer's disease and recent evidence suggests its role in Prion and Parkinson's disease. However, contradictory effects have been suggested for Huntington's disease. This difference provided a compelling reason to investigate the effect of curcumin on glutamine-rich (Q-rich) and non-glutamine-rich (non Q-rich) amyloid aggregates in the well established yeast model system. Curcumin significantly inhibited the formation of htt72Q-GFP (a Q-rich) and Het-s-GFP (a non Q-rich) aggregates in yeast. We show that curcumin prevents htt72Q-GFP aggregation by down regulating Vps36, a component of the ESCRT-II (Endosomal sorting complex required for transport). Moreover, curcumin disrupted the htt72Q-GFP aggregates that were pre-formed in yeast and cured the yeast prion, [PSI(+)].

Molecular motors often work in teams to move a cellular cargo. Yet measuring the forces exerted by each motor is challenging. Using a sensor made with denatured ssDNA and multi-color fluorescence, we measured picoNewtons of forces and nanometer distances exerted by individual constrained kinesin-1 motors acting together while driving a common microtubule in vitro . We find that kinesins primarily exerted less than 1 pN force, even while the microtubule is bypassing artificial obstacles of 20-100 nanometer size. Occasionally, individual forces increase upon encountering obstacles, although at other times they do not, with the cargo continuing in a directional manner. Our high-throughput technique, which can measure forces by many motors simultaneously, is expected to be useful for many different types of molecular motors.

Background & Objectives: In India several models of health care delivery have been explored to increase access to skilled obstetric care in rural areas, where there is a lack of specialists and appropriate facilities. We present here an innovative and affordable approach to the delivery of antenatal and obstetric care provided by the Dangoria Charitable Trust (DCT) since 1979, twinning a not-for-profit hospital in rural Andhra Pradesh with a for-profit one in the capital Hyderabad., Methods: A retrospective observational study of a random sample of the deliveries performed from 1979 to 2009 by the Dangoria Charitable Trust, based on the maternity hospital birth register, was conducted. The profile of mothers, such as their age, parity and previous miscarriages, as well as type of delivery, gender and birth weight of the newborn, and frequency of stillbirths and in hospital deaths as they evolved over time were presented using simple descriptive methods. The risk of stillbirth and in hospital death over time was explored by logistic regression after allowance for selected factors., Results: From 1979 to 2009 the cumulative number of deliveries at the Narsapur maternity hospital was 9333, from a few dozens per year in the early 1980s to over 1000 in 2009. The number of primiparae significantly increased over time, while the percentage of low birth weight babies (less than 2.5 kg) did not change appreciably. Caesarean section increased significantly over time, from 8.6 per cent in the first decade to 20.3 per cent in the last. The risk of death (stillbirths and in hospital death) consistently decreased over time, reaching 15 per thousand in the last decade. The results of a logistic regression adjusted for potential confounders showed that low birth weight babies had 4 times the risk of dying as compared to those weighing 2.5 kg or above., Conclusions: Over the 30 year period the percentage of babies discharged alive from DCT improved considerably. Caesarean sections increased significantly from the first decade to the third decade. The model adopted by the DCT to improve maternal and child health in rural areas could be replicated in other rural parts of the country.